A deterministic approach to regularized linear discriminant analysis

NEUROCOMPUTING

卷:151

Article

Sharma, Alok^1,2  Paliwal, Kuldip K.¹ 

[1] Griffith Univ, Sch Engn, Brisbane, Qld 4111, Australia

[2] Univ S Pacific, Sch Phys & Engn, Suva, Fiji

关键词: Linear discriminant analysis (LDA);    Regularized LDA;    Deterministic approach;    Cross-validation;    Classification;   

DOI  :  10.1016/j.neucom.2014.09.051

来源: Elsevier

【 摘 要 】

The regularized linear discriminant analysis (RLDA) technique is one of the popular methods for dimensionality reduction used for small sample size problems. In this technique, regularization parameter is conventionally computed using a cross-validation procedure. In this paper, we propose a deterministic way of computing the regularization parameter in RLDA for small sample size problem. The computational cost of the proposed deterministic RLDA is significantly less than the cross-validation based RLDA technique. The deterministic RLDA technique is also compared with other popular techniques on a number of datasets and favorable results are obtained. (C) 2014 Elsevier B.V. All rights reserved.

【 授权许可】

Free